Video processing using a spectral decomposition layer

ABSTRACT

A method is presented. The method includes receiving a first sequence of frames depicting a dynamic element. The method also includes decomposing each spatial position from multiple spatial positions in the first sequence of frames to a frequency domain. The method further includes determining a distribution of spectral power density over a range of frequencies of the multiple spatial positions. The method still further includes generating a first set of feature maps based on the determined distribution of spectral power density over the range of frequencies. The method still further includes estimating a first physical property of the dynamic element.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/827,456 titled “Video Processing Using a Spectral DecompositionLayer,” filed Mar. 23, 2020, and claims the benefit of U.S. ProvisionalPatent Application No. 62/821,947 titled “ITERATIVE REFINEMENT OFPHYSICS SIMULATIONS,” filed on Mar. 21, 2019. These documents areexpressly incorporated by reference herein in their entirety.

BACKGROUND Field

Aspects of the present disclosure generally relate to adaptiverefinement of physics simulations.

Background

Artificial neural networks may comprise interconnected groups ofartificial neurons (e.g., neuron models). The artificial neural networkmay be a computational device or represented as a method to be performedby a computational device. Convolutional neural networks, such as deepconvolutional neural networks, are a type of feed-forward artificialneural network. Convolutional neural networks may include layers ofneurons that may be configured in a tiled receptive field.

Deep convolutional neural networks (DCNs) are used in varioustechnologies, such as vision systems, speech recognition, autonomousdriving, and Internet of Things (IoT) devices. Conventional neuralnetwork vision systems may be used for object detection andthree-dimensional (3D) reconstruction. Scene understanding is also agoal of a neural network vision system. Specifically, it is desirable toestimate physical properties of an object from a visual observation ofthe object.

SUMMARY

In one aspect of the present disclosure, a method is disclosed. Themethod includes receiving a first sequence of frames. The method alsoincludes decomposing each spatial position from multiple spatialpositions in the first sequence of frames to a frequency domain. Themethod further includes determining a distribution of spectral powerdensity over a range of frequencies of the multiple spatial positions.The method still further includes generating a first set of feature mapsbased on the determined distribution of spectral power density over therange of frequencies.

Another aspect of the present disclosure is directed to an apparatus.The apparatus also includes means for receiving a first sequence offrames. The apparatus further includes means for decomposing eachspatial position from multiple spatial positions in the first sequenceof frames to a frequency domain. The apparatus still further includesmeans for determining a distribution of spectral power density over arange of frequencies of the multiple spatial positions. The apparatusstill further includes means for generating a first set of feature mapsbased on the determined distribution of spectral power density over therange of frequencies.

In another aspect of the present disclosure, a non-transitorycomputer-readable medium with non-transitory program code recordedthereon is disclosed. The program code is executed by a processor andincludes program code to receive a first sequence of frames. The programcode also includes program code to decompose each spatial position frommultiple spatial positions in the first sequence of frames to afrequency domain. The program code further includes program code todetermine a distribution of spectral power density over a range offrequencies of the multiple spatial positions. The program code stillfurther includes program code to generate a first set of feature mapsbased on the determined distribution of spectral power density over therange of frequencies.

Another aspect of the present disclosure is directed to an apparatus.The apparatus having a memory and one or more processors coupled to thememory. The processor(s) is configured to receive a first sequence offrames. The processor(s) is also configured to decompose each spatialposition from multiple spatial positions in the first sequence of framesto a frequency domain. The processor(s) is further configured todetermine a distribution of spectral power density over a range offrequencies of the multiple spatial positions. The processor(s) is stillfurther configured to generate a first set of feature maps based on thedetermined distribution of spectral power density over the range offrequencies.

This has outlined, rather broadly, the features and technical advantagesof the present disclosure in order that the detailed description thatfollows may be better understood. Additional features and advantages ofthe disclosure will be described below. It should be appreciated bythose skilled in the art that this disclosure may be readily utilized asa basis for modifying or designing other structures for carrying out thesame purposes of the present disclosure. It should also be realized bythose skilled in the art that such equivalent constructions do notdepart from the teachings of the disclosure as set forth in the appendedclaims. The novel features, which are believed to be characteristic ofthe disclosure, both as to its organization and method of operation,together with further objects and advantages, will be better understoodfrom the following description when considered in connection with theaccompanying figures. It is to be expressly understood, however, thateach of the figures is provided for the purpose of illustration anddescription only and is not intended as a definition of the limits ofthe present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present disclosure willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify correspondingly throughout.

FIG. 1 illustrates an example implementation of designing a neuralnetwork using a system-on-a-chip (SOC), including a general-purposeprocessor in accordance with certain aspects of the present disclosure.

FIGS. 2A, 2B, and 2C are diagrams illustrating a neural network inaccordance with aspects of the present disclosure.

FIG. 2D is a diagram illustrating an exemplary deep convolutionalnetwork (DCN) in accordance with aspects of the present disclosure.

FIG. 3 is a block diagram illustrating an exemplary deep convolutionalnetwork (DCN) in accordance with aspects of the present disclosure.

FIG. 4 illustrates an example of an iterative refinement framework inaccordance with aspects of the present disclosure.

FIG. 5 illustrates a spectral decomposition layer in accordance withaspects of the present disclosure.

FIG. 6 illustrates a flow diagram for a method in accordance withaspects of the present disclosure.

DETAILED DESCRIPTION

The detailed description set forth below, in connection with theappended drawings, is intended as a description of variousconfigurations and is not intended to represent the only configurationsin which the concepts described herein may be practiced. The detaileddescription includes specific details for the purpose of providing athorough understanding of the various concepts. However, it will beapparent to those skilled in the art that these concepts may bepracticed without these specific details. In some instances, well-knownstructures and components are shown in block diagram form in order toavoid obscuring such concepts.

Based on the teachings, one skilled in the art should appreciate thatthe scope of the disclosure is intended to cover any aspect of thedisclosure, whether implemented independently of or combined with anyother aspect of the disclosure. For example, an apparatus may beimplemented or a method may be practiced using any number of the aspectsset forth. In addition, the scope of the disclosure is intended to coversuch an apparatus or method practiced using other structure,functionality, or structure and functionality in addition to or otherthan the various aspects of the disclosure set forth. It should beunderstood that any aspect of the disclosure disclosed may be embodiedby one or more elements of a claim.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any aspect described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother aspects.

Although particular aspects are described herein, many variations andpermutations of these aspects fall within the scope of the disclosure.Although some benefits and advantages of the preferred aspects arementioned, the scope of the disclosure is not intended to be limited toparticular benefits, uses or objectives. Rather, aspects of thedisclosure are intended to be broadly applicable to differenttechnologies, system configurations, networks and protocols, some ofwhich are illustrated by way of example in the figures and in thefollowing description of the preferred aspects. The detailed descriptionand drawings are merely illustrative of the disclosure rather thanlimiting, the scope of the disclosure being defined by the appendedclaims and equivalents thereof.

A goal of scene understanding is to estimate physical properties of anobject from a visual observation of the object. Due to a number ofunderlying physical parameters, such as material properties and externalforces, conventional vision systems may not generate accurate physicalproperty estimations from visual observations. Aspects of the presentdisclosure are directed to improving a vision system's ability toestimate physical properties of an object from visual observations.

As an example, latent physical properties for a dynamic element, such ascloth blowing in the wind, may be estimated by a neural network togenerate a simulation of the dynamic element. In one configuration, aniterative refinement procedure gradually learns physical properties bycomparing a simulation of the dynamic element to a real-worldobservation. An embedding function that maps physically similar examplesto nearby points may compare the simulation to the real-worldobservation.

Specifically, a simulation may be compared to a decomposed real-worldobservation. A spectral layer may be used to decompose the simulationand real-world observation to their temporal spectral power andcorresponding frequencies. The physical properties may be iterativelyupdated based on the comparison. That is, the spectral layer (e.g.,spectral decomposition layer) computes a representation (e.g., temporalspectral power) that is suitable for domain adaptation, such that aquality of a simulated video is above a threshold. The representation isan improvement to conventional neural networks that are prone tooverfitting on pixel differences that do not accurately generatesimulated videos.

FIG. 1 illustrates an example implementation of a system-on-a-chip (SOC)100, which may include a central processing unit (CPU) 102 or amulti-core CPU configured for generating a synthetic representation of aphysical object in accordance with certain aspects of the presentdisclosure. Variables (e.g., neural signals and synaptic weights),system parameters associated with a computational device (e.g., neuralnetwork with weights), delays, frequency bin information, and taskinformation may be stored in a memory block associated with a neuralprocessing unit (NPU) 108, in a memory block associated with a CPU 102,in a memory block associated with a graphics processing unit (GPU) 104,in a memory block associated with a digital signal processor (DSP) 106,in a memory block 118, or may be distributed across multiple blocks.Instructions executed at the CPU 102 may be loaded from a program memoryassociated with the CPU 102 or may be loaded from a memory block 118.

The SOC 100 may also include additional processing blocks tailored tospecific functions, such as a GPU 104, a DSP 106, a connectivity block110, which may include fifth generation (5G) connectivity, fourthgeneration long term evolution (4G LTE) connectivity, Wi-Ficonnectivity, USB connectivity, Bluetooth connectivity, and the like,and a multimedia processor 112 that may, for example, detect andrecognize gestures. In one implementation, the NPU is implemented in theCPU, DSP, and/or GPU. The SOC 100 may also include a sensor processor114, image signal processors (ISPs) 116, and/or navigation module 120,which may include a global positioning system.

The SOC 100 may be based on an ARM instruction set. In an aspect of thepresent disclosure, the instructions loaded into the general-purposeprocessor 102 may comprise code to receive a first sequence of frames.The general-purpose processor 102 may also comprise code to decomposeeach spatial position from multiple spatial positions in the firstsequence of frames to a frequency domain. The general-purpose processor102 may further comprise code to determine a distribution of spectralpower density over a range of frequencies of the multiple spatialpositions. The general-purpose processor 102 may still further comprisecode to generate a first set of feature maps based on the determineddistribution of spectral power density over the range of frequencies.

Deep learning architectures may perform an object recognition task bylearning to represent inputs at successively higher levels ofabstraction in each layer, thereby building up a useful featurerepresentation of the input data. In this way, deep learning addresses amajor bottleneck of traditional machine learning. Prior to the advent ofdeep learning, a machine learning approach to an object recognitionproblem may have relied heavily on human engineered features, perhaps incombination with a shallow classifier. A shallow classifier may be atwo-class linear classifier, for example, in which a weighted sum of thefeature vector components may be compared with a threshold to predict towhich class the input belongs. Human engineered features may betemplates or kernels tailored to a specific problem domain by engineerswith domain expertise. Deep learning architectures, in contrast, maylearn to represent features that are similar to what a human engineermight design, but through training. Furthermore, a deep network maylearn to represent and recognize new types of features that a humanmight not have considered.

A deep learning architecture may learn a hierarchy of features. Ifpresented with visual data, for example, the first layer may learn torecognize relatively simple features, such as edges, in the inputstream. In another example, if presented with auditory data, the firstlayer may learn to recognize spectral power in specific frequencies. Thesecond layer, taking the output of the first layer as input, may learnto recognize combinations of features, such as simple shapes for visualdata or combinations of sounds for auditory data. For instance, higherlayers may learn to represent complex shapes in visual data or words inauditory data. Still higher layers may learn to recognize common visualobjects or spoken phrases.

Deep learning architectures may perform especially well when applied toproblems that have a natural hierarchical structure. For example, theclassification of motorized vehicles may benefit from first learning torecognize wheels, windshields, and other features. These features may becombined at higher layers in different ways to recognize cars, trucks,and airplanes.

Neural networks may be designed with a variety of connectivity patterns.In feed-forward networks, information is passed from lower to higherlayers, with each neuron in a given layer communicating to neurons inhigher layers. A hierarchical representation may be built up insuccessive layers of a feed-forward network, as described above. Neuralnetworks may also have recurrent or feedback (also called top-down)connections. In a recurrent connection, the output from a neuron in agiven layer may be communicated to another neuron in the same layer. Arecurrent architecture may be helpful in recognizing patterns that spanmore than one of the input data chunks that are delivered to the neuralnetwork in a sequence. A connection from a neuron in a given layer to aneuron in a lower layer is called a feedback (or top-down) connection. Anetwork with many feedback connections may be helpful when therecognition of a high-level concept may aid in discriminating theparticular low-level features of an input.

The connections between layers of a neural network may be fullyconnected or locally connected. FIG. 2A illustrates an example of afully connected neural network 202. In a fully connected neural network202, a neuron in a first layer may communicate its output to everyneuron in a second layer, so that each neuron in the second layer willreceive input from every neuron in the first layer. FIG. 2B illustratesan example of a locally connected neural network 204. In a locallyconnected neural network 204, a neuron in a first layer may be connectedto a limited number of neurons in the second layer. More generally, alocally connected layer of the locally connected neural network 204 maybe configured so that each neuron in a layer will have the same or asimilar connectivity pattern, but with connections strengths that mayhave different values (e.g., 210, 212, 214, and 216). The locallyconnected connectivity pattern may give rise to spatially distinctreceptive fields in a higher layer, because the higher layer neurons ina given region may receive inputs that are tuned through training to theproperties of a restricted portion of the total input to the network.

One example of a locally connected neural network is a convolutionalneural network. FIG. 2C illustrates an example of a convolutional neuralnetwork 206. The convolutional neural network 206 may be configured suchthat the connection strengths associated with the inputs for each neuronin the second layer are shared (e.g., 208). Convolutional neuralnetworks may be well suited to problems in which the spatial location ofinputs is meaningful.

One type of convolutional neural network is a deep convolutional network(DCN). FIG. 2D illustrates a detailed example of a DCN 200 designed torecognize visual features from an image 226 input from an imagecapturing device 230, such as a car-mounted camera. The DCN 200 of thecurrent example may be trained to identify traffic signs and a numberprovided on the traffic sign. Of course, the DCN 200 may be trained forother tasks, such as identifying lane markings or identifying trafficlights.

The DCN 200 may be trained with supervised learning. During training,the DCN 200 may be presented with an image, such as the image 226 of aspeed limit sign, and a forward pass may then be computed to produce anoutput 222. The DCN 200 may include a feature extraction section and aclassification section. Upon receiving the image 226, a convolutionallayer 232 may apply convolutional kernels (not shown) to the image 226to generate a first set of feature maps 218. As an example, theconvolutional kernel for the convolutional layer 232 may be a 5×5 kernelthat generates 28×28 feature maps. In the present example, because fourdifferent feature maps are generated in the first set of feature maps218, four different convolutional kernels were applied to the image 226at the convolutional layer 232. The convolutional kernels may also bereferred to as filters or convolutional filters.

The first set of feature maps 218 may be subsampled by a max poolinglayer (not shown) to generate a second set of feature maps 220. The maxpooling layer reduces the size of the first set of feature maps 218.That is, a size of the second set of feature maps 220, such as 14×14, isless than the size of the first set of feature maps 218, such as 28×28.The reduced size provides similar information to a subsequent layerwhile reducing memory consumption. The second set of feature maps 220may be further convolved via one or more subsequent convolutional layers(not shown) to generate one or more subsequent sets of feature maps (notshown).

In the example of FIG. 2D, the second set of feature maps 220 isconvolved to generate a first feature vector 224. Furthermore, the firstfeature vector 224 is further convolved to generate a second featurevector 228. Each feature of the second feature vector 228 may include anumber that corresponds to a possible feature of the image 226, such as“sign,” “60,” and “100.” A softmax function (not shown) may convert thenumbers in the second feature vector 228 to a probability. As such, anoutput 222 of the DCN 200 is a probability of the image 226 includingone or more features.

In the present example, the probabilities in the output 222 for “sign”and “60” are higher than the probabilities of the others of the output222, such as “30,” “40,” “50,” “70,” “80,” “90,” and “100”. Beforetraining, the output 222 produced by the DCN 200 is likely to beincorrect. Thus, an error may be calculated between the output 222 and atarget output. The target output is the ground truth of the image 226(e.g., “sign” and “60”). The weights of the DCN 200 may then be adjustedso the output 222 of the DCN 200 is more closely aligned with the targetoutput.

To adjust the weights, a learning algorithm may compute a gradientvector for the weights. The gradient may indicate an amount that anerror would increase or decrease if the weight were adjusted. At the toplayer, the gradient may correspond directly to the value of a weightconnecting an activated neuron in the penultimate layer and a neuron inthe output layer. In lower layers, the gradient may depend on the valueof the weights and on the computed error gradients of the higher layers.The weights may then be adjusted to reduce the error. This manner ofadjusting the weights may be referred to as “back propagation” as itinvolves a “backward pass” through the neural network.

In practice, the error gradient of weights may be calculated over asmall number of examples, so that the calculated gradient approximatesthe true error gradient. This approximation method may be referred to asstochastic gradient descent. Stochastic gradient descent may be repeateduntil the achievable error rate of the entire system has stoppeddecreasing or until the error rate has reached a target level. Afterlearning, the DCN may be presented with new images (e.g., the speedlimit sign of the image 226) and a forward pass through the network mayyield an output 222 that may be considered an inference or a predictionof the DCN.

Deep belief networks (DBNs) are probabilistic models comprising multiplelayers of hidden nodes. DBNs may be used to extract a hierarchicalrepresentation of training data sets. A DBN may be obtained by stackingup layers of Restricted Boltzmann Machines (RBMs). An RBM is a type ofartificial neural network that can learn a probability distribution overa set of inputs. Because RBMs can learn a probability distribution inthe absence of information about the class to which each input should becategorized, RBMs are often used in unsupervised learning. Using ahybrid unsupervised and supervised paradigm, the bottom RBMs of a DBNmay be trained in an unsupervised manner and may serve as featureextractors, and the top RBM may be trained in a supervised manner (on ajoint distribution of inputs from the previous layer and target classes)and may serve as a classifier.

Deep convolutional networks (DCNs) are networks of convolutionalnetworks, configured with additional pooling and normalization layers.DCNs have achieved state-of-the-art performance on many tasks. DCNs canbe trained using supervised learning in which both the input and outputtargets are known for many exemplars and are used to modify the weightsof the network by use of gradient descent methods.

DCNs may be feed-forward networks. In addition, as described above, theconnections from a neuron in a first layer of a DCN to a group ofneurons in the next higher layer are shared across the neurons in thefirst layer. The feed-forward and shared connections of DCNs may beexploited for fast processing. The computational burden of a DCN may bemuch less, for example, than that of a similarly sized neural networkthat comprises recurrent or feedback connections.

The processing of each layer of a convolutional network may beconsidered a spatially invariant template or basis projection. If theinput is first decomposed into multiple channels, such as the red,green, and blue channels of a color image, then the convolutionalnetwork trained on that input may be considered three-dimensional, withtwo spatial dimensions along the axes of the image and a third dimensioncapturing color information. The outputs of the convolutionalconnections may be considered to form a feature map in the subsequentlayer, with each element of the feature map (e.g., 220) receiving inputfrom a range of neurons in the previous layer (e.g., feature maps 218)and from each of the multiple channels. The values in the feature mapmay be further processed with a non-linearity, such as a rectification,max(0, x). Values from adjacent neurons may be further pooled, whichcorresponds to down sampling, and may provide additional localinvariance and dimensionality reduction. Normalization, whichcorresponds to whitening, may also be applied through lateral inhibitionbetween neurons in the feature map.

The performance of deep learning architectures may increase as morelabeled data points become available or as computational powerincreases. Modern deep neural networks are routinely trained withcomputing resources that are thousands of times greater than what wasavailable to a typical researcher just fifteen years ago. Newarchitectures and training paradigms may further boost the performanceof deep learning. Rectified linear units may reduce a training issueknown as vanishing gradients. New training techniques may reduceover-fitting and thus enable larger models to achieve bettergeneralization. Encapsulation techniques may abstract data in a givenreceptive field and further boost overall performance.

FIG. 3 is a block diagram illustrating a deep convolutional network 350.The deep convolutional network 350 may include multiple different typesof layers based on connectivity and weight sharing. As shown in FIG. 3,the deep convolutional network 350 includes the convolution blocks 354A,354B. Each of the convolution blocks 354A, 354B may be configured with aconvolution layer (CONV) 356, a normalization layer (LNorm) 358, and amax pooling layer (MAX POOL) 360.

The convolution layers 356 may include one or more convolutionalfilters, which may be applied to the input data to generate a featuremap. Although only two of the convolution blocks 354A, 354B are shown,the present disclosure is not so limiting, and instead, any number ofthe convolution blocks 354A, 354B may be included in the deepconvolutional network 350 according to design preference. Thenormalization layer 358 may normalize the output of the convolutionfilters. For example, the normalization layer 358 may provide whiteningor lateral inhibition. The max pooling layer 360 may provide downsampling aggregation over space for local invariance and dimensionalityreduction.

The parallel filter banks, for example, of a deep convolutional networkmay be loaded on a CPU 102 or GPU 104 of an SOC 100 to achieve highperformance and low power consumption. In alternative embodiments, theparallel filter banks may be loaded on the DSP 106 or an ISP 116 of anSOC 100. In addition, the deep convolutional network 350 may accessother processing blocks that may be present on the SOC 100, such assensor processor 114 and navigation module 120, dedicated, respectively,to sensors and navigation.

The deep convolutional network 350 may also include one or more fullyconnected layers 362 (FC1 and FC2). The deep convolutional network 350may further include a logistic regression (LR) layer 364. Between eachlayer 356, 358, 360, 362, 364 of the deep convolutional network 350 areweights (not shown) that are to be updated. The output of each of thelayers (e.g., 356, 358, 360, 362, 364) may serve as an input of asucceeding one of the layers (e.g., 356, 358, 360, 362, 364) in the deepconvolutional network 350 to learn hierarchical feature representationsfrom input data 352 (e.g., images, audio, video, sensor data and/orother input data) supplied at the first of the convolution blocks 354A.The output of the deep convolutional network 350 is a classificationscore 366 for the input data 352. The classification score 366 may be aset of probabilities, where each probability is the probability of theinput data including a feature from a set of features.

As discussed, it is desirable to train a simulation model to estimatephysical parameters of an object (e.g., dynamic object) or naturalphenomenon. The physical parameters may be used to generate a rendering(e.g., simulation) of the object or natural phenomenon. Examples ofobjects and natural phenomena include flags blowing in the wind, hangingfabric, smoke, draped clothing, swaying trees, fire, and water. Forbrevity, the objects and natural phenomenon may be referred to asdynamic elements.

Aspects of the present disclosure are directed to an iterative feedbackloop for adjusting physical parameters based on real-world visualobservations. In one configuration, synthetic data is used to train thevision system (e.g., deep neural network). For illustrative purposes,aspects of the present disclosure are described with respect to fabric(e.g., cloth) that is subject to external forces (e.g., wind). However,aspects of the present disclosure may be applicable to other dynamicelements. The iterative feedback loop for adjusting physics simulationsbased on visual observations may be referred to as an iterativerefinement loop.

In some cases, physical parameters (e.g., material properties) of adynamic element may be inferred from visual observations. Still, merelyinferring physical parameters of the dynamic element from a visualobservation (e.g., video) may lead to an inaccurate model. That is, whenusing simulations and renderings for learning, a visually appealingrendering does not necessarily imply a realistic rendering. Therefore,it is desirable to determine a similarity of underlying physicalparameters rather than a visual correspondence.

Aspects of the present disclosure improve physical parameter estimationsby iteratively tuning the physical parameter estimations based on visualobservations. Specifically, aspects of the present disclosure tune theparameters based on a physical similarity of real and simulatedobservations.

In one configuration, a simulation engine determines physical parametersof a dynamic element to generate material property estimations (e.g. 3Dmeshes, point clouds, flow vectors). A rendering engine may use thematerial property estimations to render a simulation (e.g., an image orvideo) of the dynamic element. The simulation may be compared with areal-world observation of the dynamic element.

The real-world observation may include ground-truth external forcemeasurements (e.g., wind speed gauged with an anemometer). An iterativeprocess refines the physical parameters by maximizing a physicalsimilarity between a real-world observation and a simulation. Thesimilarity may be determined by a comparison model. Realism of thesimulations may be improved by updating the physical parameters.

Aspects of the present disclosure train the comparison model using onlysimulations of dynamic elements (e.g., without observing a real-worldmanifestation of the dynamic elements). The comparison model determinesa similarity of two visual observations based on a distance metric. Thecomparison model may be implemented as a Siamese network trained with acontrastive loss.

In one configuration, the iterative refinement framework receives areal-world video segment of a dynamic element. The real-world dynamicelement may be, for example, a flag in the wind, hanging fabric, smoke,draped clothing, swaying trees, fire, or water. The real-world videosegment may be obtained from a video dataset of dynamic elementssubjected to external forces (e.g., flags waving in the wind).

The iterative refinement framework may be initialized using pre-definedphysical parameters (e.g., material properties and/or external forces).As an example, the iterative process is initialized with pre-definedmaterial properties (e.g., nylon) and a pre-defined external force(e.g., wind). A synthetic video segment is rendered from arepresentation of the pre-defined material properties and thepre-defined external force. The pre-defined material properties and thepre-defined external force of the synthetic video segment may be tunedbased on comparisons with real-world observations of the materialproperties and the external force.

To simulate dynamic elements (e.g., flags in the wind, hanging fabric,smoke, draped clothing, swaying trees, fire, or water), a simulator,such as an ARCSim physics simulator, renders a simulation of a physicalobject (e.g., cloth) based on physical parameters, such as materialproperties and external forces (e.g., gravity and wind). The simulatormay include pre-defined fabric types. Still, the exact materialproperties, such as bending and stiffness parameters may be unknown. TheARCSim physics simulator is a simulation engine for animating sheets ofdeformable materials such as cloth, paper, plastic, and metal. Thesimulation engine uses adaptively refined meshes to resolve geometricand dynamic details of a simulated object.

In one configuration, a simulation engine generates representations ofthe pre-defined material properties and the pre-defined external force.The synthetic video segment is rendered based on the output of thesimulation engine. A similarity function determines whether a quality ofthe synthetic video segment relative to the real-world video clipsatisfies a quality threshold. The similarity function is defined basedon a first similarity function parameter associated with the real-worldvideo segment and a second similarity function parameter based on asynthetic video segment.

The similarity function may be implemented as deep neural network with aspectral decomposition layer. The similarity function may be trainedusing a contrastive loss. In one configuration, the similarity functionis trained with only simulated data. That is, the similarity function istrained without ever having a seen a real example of a dynamic element.

The iterative process further includes updating the pre-defined physicalparameters based on the comparison. In one configuration, the comparisonis based on spectral components of the synthetic video clip and thereal-world observations. The spectral components may be generated by aspectral decomposition layer. The iterative process continues until thequality of the synthetic video segment exceeds a pre-defined threshold.The synthetic video clip may be a rendered animation.

In one configuration, the spectral decomposition layer decomposes asequence of H×W frames into its temporal frequencies for each spatiallocation (e.g., pixel), where H is the height and W is the width of eachframe. In one configuration, the spectral decomposition layer generatesspectral power and frequency maps from a received sequence of frames.The spectral power and frequency maps indicate dominant frequencies foreach spatial position in the video. The spectral power and frequencymaps may also be referred to as feature maps. The spectral power andfrequency maps may be processed by a number of convolution layers tolearn a mapping between the visual observations and external forces(e.g., wind).

The iterative refinement framework improves physics simulations and maybe applied to a variety of applications, such as computer graphics,three-dimensional gaming engines, and video augmentation with syntheticstructures. Aspects of the present disclosure may also be used forvirtual clothing. For example, a virtual system for trying-on clothesmay estimate clothing properties from a video. The estimated propertiesmay be used to simulate a look of the clothing on an individual.

FIG. 4 illustrates an example of an iterative refinement framework 400according to aspects of the present disclosure. For illustrativepurposes, FIG. 4 is described with respect flags flying in the wind.Aspects of the present disclosure are not limited to flags flying in thewind. Aspects of the present disclosure may be applicable to otherobjects and/or other real-world visual phenomena such as hanging fabric,smoke, turbulence, draped clothing, swaying trees, fire, or water.

As shown in FIG. 4, the iterative refinement framework 400 includes asimulation engine 402 and a rendering engine 406. The simulation engine402 may also be referred to as a physics simulator. The simulationengine 402 selects simulation parameters Θ to generate a dataset ofintermediate representations 404 (e.g., 3D meshes, point clouds, or flowvectors). The simulation parameters Θ include intrinsic properties Θ_(i)and external forces Θ_(e) randomly sampled from a predefined searchspace. For example, the intrinsic properties Θ_(i) include fabric sizeand fabric parameters and the external forces Θ_(e) include windparameters.

In one configuration, the simulation engine 402 generatesthree-dimensional (3D) meshes 404 from the simulation parameters Θ. Thatis, the 3D meshes 404 may be one of the intermediate representations404. In the example of FIG. 4, the 3D meshes 404 are 3D cloth meshes. A3D mesh may include a set of vertices, edges, and faces defining a shapeof a 3D object. The intermediate representations 404 are input to therendering engine 406.

For each intermediate representation 404 from the dataset ofintermediate representations 404, the rendering engine 406 generatesmultiple synthetic video segments 408 with different renderingparameters. Each synthetic video segment 408 may have a dimension ofH×W, where H is a number of pixels for a height of a video frame and Wis a number of pixels for a width of the video frame. Additionally, eachsynthetic video segment 408 may include multiple frames and one or moreimage channels. In one configuration, the synthetic video segments 408simulate a dynamic element in a three-dimensional environment, where anappearance of the synthetic video segment 408 is controlled. Forexample, flag texture, scene lighting, and camera parameters may becontrolled.

Real-world video segments 410 (e.g., target video segment) of thedynamic elements in the synthetic video segments 408 are provided to theiterative refinement framework 400. The dynamic element (e.g., a flag)in the real-world video segments 410 has the same characteristics andsame external forces (e.g., wind) as the dynamic element of thesynthetic video segments 408. The real-world video segments 410 arecompared with the synthetic video segments 408.

Specifically, an embedding function 412 maps the synthetic videosegments 408 and the real-world video segments 410 to an an embeddingspace 414 (e.g., a manifold) on which physically similar examples areassigned to nearby points. A distance metric in the embedding space 414measures a similarity between the synthetic video segments 408 and thereal-world video segments 410.

A similarity function measures a similarity between the synthetic videosegments 408 and the real-world video segments 410. The similarityfunction reflects correspondence in physical dynamics between the twoinstances. In one configuration, the similarity function first learnsthe distance metric from only synthetic video segments 408. That is, thedistance metric may be learned from a contrastive loss based on positiveexample pairs and negative example pairs of synthetic video segments408.

Positive example pairs are synthetic video segments 408 originating froma same simulation (e.g., sharing simulation parameters Θ). Negativeexample pairs are synthetic video segments 408 with different simulationparameters Θ. Pairs of synthetic video segments 408 may be mapped to theembedding space 414 through the embedding function 412 in a Siamesefashion.

The embedding function may convolve the input video (e.g., syntheticvideo segments 408 and real-world video segments 410) with multiplefilters, such as a temporal Gaussian filter followed by two spatiallyoriented first-order derivative filters. Two different filtered videosare generated from the filtering. The filtered videos may be subsampled.For example, max pooling may be applied to subsample the filteredvideos.

The filtered video representations may be input to a spectraldecomposition layer to produce spectral power and frequency maps. Theoutputs are stacked into a multi-channel feature map to be furtherprocessed by convolutional layers (e.g., two-dimensional convolutionallayers). The output of the convolutional layers may be mapped to theembedding space 414.

In the embedding space 414, a physical similarity may be evaluated usinga squared Euclidean distance. If measured over a collection of syntheticvideo segments 408, the contrastive loss pulls together physicallysimilar examples, whereas physically dissimilar points are pushed apart.As a result, by training only on synthetic video segments 408, aspectsof the present disclosure learn to measure a similarity betweensynthetic video segments 408 and the real-world video segments 410.After completing the training, when observing a real video in theiterative framework, an embedding function projects both a currentsimulated video and real video to the embedding space 414. The Euclideandistance 418 computed in the embedding space 414 guides the parameteroptimization.

In one configuration, the simulation parameters Θ are iterativelyupdated based on a result of the comparison. Specifically, a parameterupdater 416 is used to minimize a distance between the synthetic videosegments 408 and real-world video segments 410. After each comparison,the parameter updater 416 updates the simulation parameters Θ until astop criteria is satisfied. The stop critieria may be satisfied when anaccuracy threshold is satisfied or an evaluation budget is consumed. Theevaluation budget may be defined by available computing resources and/ora maximum amount of time available for running the refinement (or both).The evaluation budget may be fixed or dynamic.

The parameter updater 416 outputs the refined simulation parameters Θwhen the iterative process is terminated. An optimization scheme, suchas Bayesian optimization, updates the simulation parameters Θ. In oneconfiguration, in addition to local gradient information, the simulationparameters Θ are updated based on information from previousobservations.

In one configuration, using the example of a flag flying in the wind,the rendering engine 406 generates synthetic video segments 408 using atwo-step process. First, a physics engine or simulator (e.g., the ArcSimphysics simulator) simulates dynamics of the element. Additionally,meshes are rendered in a 3D environment with full control over flagtexture, lighting, and camera position. The meshes may be rendered basedon material properties and external forces (e.g., gravity and wind). Themeshes are provided to a rendering engine to generate the renderedanimations. The material properties may include bending properties,shearing properties, and stretching properties. The aspects of thepresent disclosure match material properties obtained for the syntheticflag to the material properties of the real-world flag. Additionally,aspects of the present disclosure match external forces affecting thematerial of the real-world flag to external forces obtained for thesynthetic flag.

As discussed, the real-world video segments 410 include ground-truthexternal force measurements. For example, wind speed may be measured inreal-time (e.g., a sampling frequency of two Hertz (2 Hz)) whilerecording the videos for a real-world flag dataset. In one example, windspeed is measured by hoisting an anemometer onto a flag pole so that itis directly positioned next to the flag.

A frequency decomposition may be extracted from the video segments. Thefrequency decomposition may include changes to the frequency over asurface of the element and the direction of the change. Thus, a staticfrequency map and power map for a video segment may be obtained. Eachspectral power at a spatial location (e.g., pixel) corresponds to aparticular frequency. A frequency map may identify high and lowfrequencies regions. The spectral decomposition layer extracts thefrequency decomposition.

FIG. 5 illustrates spectral decomposition layer 500 according to aspectsof the present disclosure. The spectral decomposition layer 500 distillstemporal frequencies from a video. A spatial arrangement of lowfrequency and high frequency regions may be inferred from the distilledtemporal frequencies. As shown in FIG. 5, temporal signals 504 aregenerated from a sequence of frames 502. The sequence of frames 502 mayrepresent an input video. The sequence of frames may have amulti-dimensional relationship represented with tensors. For example,the tensors may have a shape represented by C×N_(t)×H×W, where N_(t)represents a spatial size (e.g., number of frames), C represents anumber of channels (e.g., three channels for an RGB frame), W representsa width in pixels of each frame, and H represents a height in pixels ofeach frame. FIG. 5 illustrates an example of one channel C.

In one configuration, the sequence of frames 502 is treated as acollection of signals (e.g., H×W signals) for each spatial position(e.g., pixel). The sequence of frames 502 may be reshaped to acollection of temporal signals 504. A discrete Fourier transform (DFT)maps the temporal signals 504 to a frequency domain. A spatialdistribution of a temporal spectral power of the sequence of frames 502is determined from the frequency domain mapping.

The DFT maps a signal f(N) for n ∈ [0, N_(t)−1] into the frequencydomain by:

F(jω)=Σ_(n=0) ^(N) ^(t) ⁻¹ f[n]e ^(−jωnT),  (1)

where, ω is a frequency, and j is an imaginary number of the DFT. TheDFT's output may be mapped to a real-valued representation. Theperiodogram of a signal is a representation of its spectral power and isdefined

${I(\omega)} = {\frac{1}{N_{t}}{{{F\left( {j\;\omega} \right)}}^{2}.}}$

I(ω) provides a spectral power magnitude at each sampled frequency.

In one configuration, the top-k strongest frequencies and correspondingspectral power 506 of the periodogram are selected to reducedimensionality and emphasize the video's discriminative frequencies.Given a signal of an arbitrary length, selecting the top-k frequenciesgenerates k pairs including I(ω_(max) _(i) ) and ω_(max) _(i) for i ∈[0, k], thus yielding 2k scalar values.

The spectral decomposition layer 500 generates a multi-channel featuremap 508 based on the top-k frequencies. That is, for a sequence offrames 502 (e.g., C×N_(t)×H×W), the spectral decomposition produces 2kfeature maps of size H×W (e.g., 2k C×H×W), where the factor twoindicates that both the power map and frequency map are stacked in thefinal output. When k equals one, only a single dominant frequency isselected. Additionally, when k equals two, the two most dominantfrequencies in the signal are selected. The output representation may bea feature map with multiple channels and can serve as input toconsecutive layers in a deep learning architecture.

The spectral decomposition layer may be a building block of a neuralnetwork architecture for performing spectral decomposition. Thus, thespectral decomposition layer is used to perform an explicit temporalspectral decomposition inside a deep learning architecture.

Prior to processing the sequence of frames 502, the sequence of frames502 may be spatially decomposed into dominant orientation bands. Forexample, Gaussian derivative filters may decompose the sequence offrames 502. Additionally, or alternatively, the sequence of frames 502may be spatially subsampled to reduce computation. The spectral powermaps and frequency maps produced by the spectral decomposition layer 500may be processed by convolutional layers. Discriminative spectral-basedfeature maps may be learned for a particular task using backpropagationthrough the trainable layers of the network.

FIG. 6 illustrates a method in accordance with aspects of the presentdisclosure. The example process 600 is an example of spectraldecomposition layer of an artificial neural network. As shown in FIG. 6,the spectral decomposition layer receives a sequence of frames (block602). The sequence of frames may be referred to as a three-dimensionalvolume.

The video may be a real-world or synthetic example of a dynamic element.A real-world example refers to an actual occurrence of a dynamic elementas opposed to a synthetically generated occurrence of a dynamic element.The dynamic element may be influenced by external forces, such as wind.Additionally, the dynamic element may be an element such as fabric,smoke, or water.

As shown in FIG. 6, in some aspects, the spectral decomposition layerdecomposes each spatial position from multiple spatial positions in thefirst sequence of frames to a frequency domain (block 604). The spatialpositions may be pixels in the sequence of frames. Additionally, thespectral decomposition layer determines a distribution of spectral powerdensity over a range of frequencies of the multiple spatial positions(block 606). Finally, as shown in FIG. 6, the spectral decompositionlayer generates a first set of feature maps based on the determineddistribution of spectral power density over the range of frequencies(block 608).

The various operations of methods described above may be performed byany suitable means capable of performing the corresponding functions.The means may include various hardware and/or software component(s)and/or module(s), including, but not limited to, a circuit, anapplication specific integrated circuit (ASIC), or processor. Generally,where there are operations illustrated in the figures, those operationsmay have corresponding counterpart means-plus-function components withsimilar numbering.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Additionally, “determining” may include receiving (e.g., receivinginformation), accessing (e.g., accessing data in a memory) and the like.Furthermore, “determining” may include resolving, selecting, choosing,establishing, and the like.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a general-purpose processor, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array signal (FPGA) or other programmable logic device(PLD), discrete gate or transistor logic, discrete hardware componentsor any combination thereof designed to perform the functions describedherein. A general-purpose processor may be a microprocessor, but in thealternative, the processor may be any commercially available processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with thepresent disclosure may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in any form of storage medium that is knownin the art. Some examples of storage media that may be used includerandom access memory (RAM), read only memory (ROM), flash memory,erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), registers, a hard disk, aremovable disk, a CD-ROM and so forth. A software module may comprise asingle instruction, or many instructions, and may be distributed overseveral different code segments, among different programs, and acrossmultiple storage media. A storage medium may be coupled to a processorsuch that the processor can read information from, and write informationto, the storage medium. In the alternative, the storage medium may beintegral to the processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

The functions described may be implemented in hardware, software,firmware, or any combination thereof. If implemented in hardware, anexample hardware configuration may comprise a processing system in adevice. The processing system may be implemented with a busarchitecture. The bus may include any number of interconnecting busesand bridges depending on the specific application of the processingsystem and the overall design constraints. The bus may link togethervarious circuits including a processor, machine-readable media, and abus interface. The bus interface may be used to connect a networkadapter, among other things, to the processing system via the bus. Thenetwork adapter may be used to implement signal processing functions.For certain aspects, a user interface (e.g., keypad, display, mouse,joystick, etc.) may also be connected to the bus. The bus may also linkvarious other circuits such as timing sources, peripherals, voltageregulators, power management circuits, and the like, which are wellknown in the art, and therefore, will not be described any further.

The processor may be responsible for managing the bus and generalprocessing, including the execution of software stored on themachine-readable media. The processor may be implemented with one ormore general-purpose and/or special-purpose processors. Examples includemicroprocessors, microcontrollers, DSP processors, and other circuitrythat can execute software. Software shall be construed broadly to meaninstructions, data, or any combination thereof, whether referred to assoftware, firmware, middleware, microcode, hardware descriptionlanguage, or otherwise. Machine-readable media may include, by way ofexample, random access memory (RAM), flash memory, read only memory(ROM), programmable read-only memory (PROM), erasable programmableread-only memory (EPROM), electrically erasable programmable Read-onlymemory (EEPROM), registers, magnetic disks, optical disks, hard drives,or any other suitable storage medium, or any combination thereof. Themachine-readable media may be embodied in a computer-program product.The computer-program product may comprise packaging materials.

In a hardware implementation, the machine-readable media may be part ofthe processing system separate from the processor. However, as thoseskilled in the art will readily appreciate, the machine-readable media,or any portion thereof, may be external to the processing system. By wayof example, the machine-readable media may include a transmission line,a carrier wave modulated by data, and/or a computer product separatefrom the device, all which may be accessed by the processor through thebus interface. Alternatively, or in addition, the machine-readablemedia, or any portion thereof, may be integrated into the processor,such as the case may be with cache and/or general register files.Although the various components discussed may be described as having aspecific location, such as a local component, they may also beconfigured in various ways, such as certain components being configuredas part of a distributed computing system.

The processing system may be configured as a general-purpose processingsystem with one or more microprocessors providing the processorfunctionality and external memory providing at least a portion of themachine-readable media, all linked together with other supportingcircuitry through an external bus architecture. Alternatively, theprocessing system may comprise one or more neuromorphic processors forimplementing the neuron models and models of neural systems describedherein. As another alternative, the processing system may be implementedwith an application specific integrated circuit (ASIC) with theprocessor, the bus interface, the user interface, supporting circuitry,and at least a portion of the machine-readable media integrated into asingle chip, or with one or more field programmable gate arrays (FPGAs),programmable logic devices (PLDs), controllers, state machines, gatedlogic, discrete hardware components, or any other suitable circuitry, orany combination of circuits that can perform the various functionalitydescribed throughout this disclosure. Those skilled in the art willrecognize how best to implement the described functionality for theprocessing system depending on the particular application and theoverall design constraints imposed on the overall system.

The machine-readable media may comprise a number of software modules.The software modules include instructions that, when executed by theprocessor, cause the processing system to perform various functions. Thesoftware modules may include a transmission module and a receivingmodule. Each software module may reside in a single storage device or bedistributed across multiple storage devices. By way of example, asoftware module may be loaded into RAM from a hard drive when atriggering event occurs. During execution of the software module, theprocessor may load some of the instructions into cache to increaseaccess speed. One or more cache lines may then be loaded into a generalregister file for execution by the processor. When referring to thefunctionality of a software module below, it will be understood thatsuch functionality is implemented by the processor when executinginstructions from that software module. Furthermore, it should beappreciated that aspects of the present disclosure result inimprovements to the functioning of the processor, computer, machine, orother system implementing such aspects.

If implemented in software, the functions may be stored or transmittedover as one or more instructions or code on a computer-readable medium.Computer-readable media include both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage medium may be anyavailable medium that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code in the form of instructions or datastructures and that can be accessed by a computer. Additionally, anyconnection is properly termed a computer-readable medium. For example,if the software is transmitted from a website, server, or other remotesource using a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared (IR),radio, and microwave, then the coaxial cable, fiber optic cable, twistedpair, DSL, or wireless technologies such as infrared, radio, andmicrowave are included in the definition of medium. Disk and disc, asused herein, include compact disc (CD), laser disc, optical disc,digital versatile disc (DVD), floppy disk, and Blu-ray® disc where disksusually reproduce data magnetically, while discs reproduce dataoptically with lasers. Thus, in some aspects computer-readable media maycomprise non-transitory computer-readable media (e.g., tangible media).In addition, for other aspects computer-readable media may comprisetransitory computer-readable media (e.g., a signal). Combinations of theabove should also be included within the scope of computer-readablemedia.

Thus, certain aspects may comprise a computer program product forperforming the operations presented herein. For example, such a computerprogram product may comprise a computer-readable medium havinginstructions stored (and/or encoded) thereon, the instructions beingexecutable by one or more processors to perform the operations describedherein. For certain aspects, the computer program product may includepackaging material.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a userterminal and/or base station can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes, and variations may be made in the arrangement, operation, anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

What is claimed is:
 1. A computer-implemented method, comprising:receiving a first sequence of frames depicting a dynamic element;decomposing each spatial position from a plurality of spatial positionsin the first sequence of frames to a frequency domain; determining adistribution of spectral power density over a range of frequencies ofthe plurality of spatial positions; generating a first set of featuremaps based on the determined distribution of spectral power density overthe range of frequencies; and estimating a first physical property ofthe dynamic element.
 2. The method of claim 1, wherein the dynamicelement comprises one of cloth, fabric, smoke, fire, water, and trees.3. The method of claim 1, wherein the first physical property is amaterial property of the dynamic element.
 4. The method of claim 3,wherein the material property estimation is in the form of a 3D mesh, apoint cloud, or a flow vector.
 5. The method of claim 3, wherein thematerial property is one of a bending property, a shearing property, anda stretching property.
 6. The method of claim 1, wherein the firstphysical property is an external force acting on the dynamic element. 7.An apparatus, comprising: a memory unit; and at least one processorcoupled to the memory unit, the at least one processor configured: toreceive a first sequence of frames depicting a dynamic element; todecompose each spatial position from a plurality of spatial positions inthe first sequence of frames to a frequency domain; to determine adistribution of spectral power density over a range of frequencies ofthe plurality of spatial positions; to generate a first set of featuremaps based on the determined distribution of spectral power density overthe range of frequencies; and to estimate a first physical property ofthe dynamic element.
 8. The apparatus of claim 7, wherein the dynamicelement comprises one of cloth, fabric, smoke, fire, water, and trees.9. The apparatus of claim 7, wherein the first physical property is amaterial property of the dynamic element.
 10. The apparatus of claim 9,wherein the material property estimation is in the form of a 3D mesh, apoint cloud, or a flow vector.
 11. The apparatus of claim 9, wherein thematerial property is one of a bending property, a shearing property, anda stretching property.
 12. The apparatus of claim 7, wherein the firstphysical property is an external force acting on the dynamic element.13. A non-transitory computer-readable medium having program coderecorded thereon, the program code executed by a processor andcomprising: program code to receive a first sequence of frames depictinga dynamic element; program code to decompose each spatial position froma plurality of spatial positions in the first sequence of frames to afrequency domain; program code to determine a distribution of spectralpower density over a range of frequencies of the plurality of spatialpositions; program code to generate a first set of feature maps based onthe determined distribution of spectral power density over the range offrequencies; program code to estimate a first physical property of thedynamic element.
 14. The non-transitory computer-readable medium ofclaim 13, wherein the dynamic element comprises one of cloth, fabric,smoke, fire, water, and trees.
 15. The non-transitory computer-readablemedium of claim 13, wherein the first physical property is a materialproperty of the dynamic element.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the material propertyestimation is in the form of a 3D mesh, a point cloud, or a flow vector.17. The non-transitory computer-readable medium of claim 15, wherein thematerial property is one of a bending property, a shearing property, anda stretching property.
 18. The non-transitory computer-readable mediumof claim 13, wherein the first physical property is an external forceacting on the dynamic element.